Gene fusions, encoding fusion oncoproteins, represent an important class of oncogenic driver aberrations which hold great promise clinically for cancer therapy. A great success of personalized cancer therapy is the case of BCR-ABL fusion oncoprotein and the use of gleevec (imatinib) or related compounds. Apart from a few classic examples heavily investigated for decades, the remaining cancer gene fusions have negligible information about their functional consequences. These examples argue that, understanding how gene fusions cause cancer will require parallel systematic platforms characterizing the molecular and mechanistic behaviors of fusion oncoproteins.

To tackle this problem, we propose...

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Gene fusions, encoding fusion oncoproteins, represent an important class of oncogenic driver aberrations which hold great promise clinically for cancer therapy. A great success of personalized cancer therapy is the case of BCR-ABL fusion oncoprotein and the use of gleevec (imatinib) or related compounds. Apart from a few classic examples heavily investigated for decades, the remaining cancer gene fusions have negligible information about their functional consequences. These examples argue that, understanding how gene fusions cause cancer will require parallel systematic platforms characterizing the molecular and mechanistic behaviors of fusion oncoproteins.

To tackle this problem, we propose the following innovative approaches: First, we will prioritize candidate driver cancer fusions using a functional variomics strategy we developed, followed by systematic characterization of these fusion oncoproteins by our established phenotyping system; Second, we will assess the functional effects of fusion oncoproteins by our high-content imaging platform; Finally, we will further investigate the molecular mechanisms underlying fusion-driven cancer using high-throughput proteomics coupled with a chemical drug discovery pipeline.

In summary, this integrative approach is innovative because it will provide novel insights in prioritizing fusion oncoproteins responsible for cancer progression, and uncovering patient-specific disease mechanisms at a higher resolution. Furthermore, the proposed research is significant because it will determine distinct signaling alterations underlying genetic heterogeneity, and present potential biomarkers and novel drug targets for more effective cancer diagnosis and treatment. Together, our proposed strategy demonstrates broad applicability and efficacy, and the primary outcome of this work, identification of actionable fusion oncoproteins to treat cancer, ultimately stands to have major clinical impact to save more patient lives from cancer.

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